AI for Nonprofits: Quick guide

This is a brief summary of my online course “IA for nonprofits”. It’s a 100% practical course tailored to the needs of nonprofit organizations and includes some great extra resources:

• Steps to successfully implement AI in your organization
• Main uses of AI in nonprofit organizations
• AI risks (and how to avoid them)
• 10 quick tactics to multiply your results with AI
• Internal AI policy template (ready to copy)
• Prompt engineering guide (how to talk to AI)

AI Myths

AI is a complex and rapidly changing field, and some people are spreading old or incorrect information, so let’s start with some common misconceptions:

⛔ “ChatGPT (and similar tools) are just dumb parrots”

✅ The best AI models are already better for many tasks than the average human (proved in many studies), so they are clearly not dumb. It’s true that AI sometimes make silly mistakes and give false info (hallucinations), but tools like ChatGPT are including new features that help with these issues (search, reasoning, deep research, website and tool use, run code…). They are not just chatbots now, they are more like “little computers” with many “programs” that you can use inside them. To get great results you must understand which AI tool, model and feature is the best for each task.

⛔ “AI = ChatGPT”

✅ AI is a generic concept that includes many different types of tools. AI include LLM-based chatbots like ChatGPT, but also tools to create images and videos, analyze datasets and create forecasts, create websites and apps, perform deep research about complex topics, connect tools and automate workflows, and a huge etcetera. Organizations that only use ChatGPT are missing out on dozens of AI tools that could help them a lot.

⛔ “AI is too risky and unethical for a nonprofit”

✅ Risks exist, but they can be mitigated through strategies such as implementing a solid AI Policy, using prompts designed to reduce risks, choosing secure tools with the right privacy settings, and using local AI tools.

⛔ “AI is only for big or tech-savvy organizations”

✅ Many AI tools require little to no technical expertise and are free or low-cost. If small organizations use AI properly, it can be an equalizer (put them at the same level of bigger organizations): They can use AI tools for specific tasks or entire projects that were out their reach because they required a lot of staff or expensive consultants.

⛔ “AI will replace human jobs”

✅ AI is better at augmenting human capabilities than fully replacing them. In the short term, it will mostly impact low-value and repetitive tasks, allowing people to focus on high-value tasks. In the long term, some jobs might disappear, but new work opportunities will emerge (like it already happened with every new tech revolution).

Benefits of AI for Nonprofits

• Increased efficiency and productivity: AI can automate routine tasks, freeing up staff time for more strategic activities.
• Reduced costs: AI can help optimize resource allocation and reduce operational expenses.
• Improved decision-making and problem-solving: AI can analyze large datasets and identify patterns to support complex problem-solving and strategic decision-making.
• Enhanced impact: AI can help nonprofits achieve their missions more effectively by improving program delivery, fundraising, and many other tasks.

Risks and challenges of AI for Nonprofits

• Ethical considerations: AI raises ethical concerns related to fairness, accountability, and transparency.
• Privacy risks: AI systems may collect and use personal data in ways that violate privacy.
• Cybersecurity threats: AI systems can be vulnerable to cyberattacks, potentially leading to data breaches and operational disruptions.
• Algorithmic bias: AI models trained on biased data can perpetuate and amplify existing societal biases.
• Data availability and quality: Training AI models require high-quality data, which may be unavailable or difficult to access for some organizations.
• Lack of a strategic vision: Organizations may adopt AI without a clear plan for how to integrate it effectively, leading to lackluster results and fading interest in AI-related projects.

Use Cases for AI in Nonprofits (Examples)

• Strategy & Brainstorming: Developing new campaign ideas, AI-assisted SWOT analysis, optimizing resource allocation.
• Data Analysis: Analyzing donor and fundraising data, automating survey and feedback analysis, predictive modeling, anomaly detection.
• Content Creation: Generating blog posts, newsletters, and reports, creating presentations and slides, generating social media posts & graphics, writing fundraising & grant proposals.
• Multimedia Creation: Creating & editing videos, generating voiceovers & podcasts, image and design generation.
• Translation & Accessibility: Translating content & documents, improving accessibility for disabled users.
• Event Planning & Management: Automating event registration & management, enhancing virtual & hybrid events, predicting event attendance & optimize engagement.
• Automation: Automating data entry & processing, AI-powered email & calendar management, workflow automation & task management.
• Chatbots & AI Assistants: Creating AI chatbots for donor & volunteer questions, automating internal staff support.
• Coding & Web Design: AI-assisted website development, automated chatbot integration, debugging & code optimization.
• Research & text analysis: Find & summarize information, Extract data from unstructured documents (PDFs, scans, etc.).

Roadmap for AI Implementation:

A successful AI implementation isn’t about simply adopting the latest technology. It requires a strategic approach that aligns with your organization’s mission, values, and resources. You need the correct priorities, people and tools. And probably start with small pilots before launching more ambitious projects.

1. Assemble your AI Core Team: Building the right team is essential. It shouldn’t be solely an IT function. Aim for a diverse group with a mix of skills and perspectives. You should define the roles and responsibilities, so everyone understands their contribution and prevents overlap or gaps. You might want to design an “AI Officer” that is in charge of most day-to-day tasks.
2. Identify high-impact AI opportunities: Don’t start with the technology; start with your mission. Remember that the AI uses can be internal (eg. automating processes, analyzing data, etc.) or external (eg. giving info or services to beneficiaries, volunteers, donors, etc.). And once you have a big list of potential AI use cases, you need a way to prioritize them (impact, cost, risk).
3. Select the right AI tools: Choose the right tools and technologies to bring those ideas to life. This can be a difficult task, as the AI landscape is vast (thousands of different tools and providers) and constantly evolving. When comparing tools, consider features, cost, integrations, security, support, etc. You might want to create a checklist with all your requirements.
4. Launch small pilots projects and iterate: The key to successful AI implementation, especially for nonprofits with limited resources, is to start small, learn fast, and iterate.
5. Develop your AI Policy: Establish clear guidelines and principles for its responsible and ethical use. Mitigates legal, ethical, and reputational risks associated with AI.
6. Build your AI Knowledge Base (the “AI Library”): The AI Library is a living document that evolves over time as your organization gains experience with AI. It’s a place to store best practices, lessons learned and helpful resources. It promotes knowledge-sharing, consistency, efficiency, and continuous learning.

Next steps